This thesis is concerned with developing MCMC methodology for sampling from given target distributions. In particular we are interested in situations where sampling is difficult using current techniques, and where we use methods which can be seen as exact approximations of preferable algorithms that for various reasons might be unavailable or impractical. We develop algorithms that attempt to reduce the autocorrelation seen in the output of MCMC samplers using the principle of local approximation, and we explore the performance of existing methods that apply in situations where the target density may not be evaluated at every point. This leads to guidelines that suggest how computational resources should best be allocated to yield low-variance estimators.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:680131 |
| Date | January 2013 |
| Creators | Cainey, Joe |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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